rosnet 0.0.1

Causal discovery with ML

rosnet

설명 / Description

🇰🇷 ‘rosnet’ 은 ML을 적용한 causal discovery 패키지입니다. 제 개인 연구를 위해 만들었지만, 다른 사람들도 최대한 쓰기 쉽도록 설계했습니다.

🔠 ‘rosnet’ is causal discovery package applied ML . I made it for my personal study. But, it is designed to be used as easy for others as possible.

목적 / Purpose

🇰🇷 이 패키지의 목적은 다음과 같습니다 :

• ML 알고리즘을 Causal discovery에 적용
• 텐서 기반으로 기존 ML 알고리즘 재설계

🔠 The purpose of this package is as follows :

• Applying ML algorithm to Causal discovery
• Re-engineering existing ML algorithm based on tensor

설치 / Installment

!pip install rosnet

🔔 요구 패키지 / Required package

• numpy

사용법 / Manual

🇰🇷 이 패키지의 API는 scikit-learn, keras 와 거의 비슷합니다!

• 오직 fit 과 predict, 두 개의 함수만 사용하시면 됩니다.

🔠 API of this package is just like scikit-learn and keras!

• You only need to use two functions: fit and predict.

예시 / Example

# Multilayer Perceptron

# **Notice** : I made some ML algorithm as needed, but not all of them.
#          If you just want to use ML algorithm itself, 
#          it is recommened to use other ML packages like scikit-learn, tensorflow ...

from rosnet.neural_network import layers
import rosnet.neural_network as network

X_train = # Your code, numpy.narray expected 
y_train = # Your code, numpy.narray expected

def build_model():
  model = network.Sequential([
    layers.Dense(64, activation='relu', input_shape=(X_train.shape[1], )),
    layers.Dense(64, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(4)
  ])

  optimizer = network.optimizers.SGD(0.001)

  model.compile(loss='mse',
                optimizer=optimizer,
                metrics=['mae', 'mse'])
  return model

model = build_model()
model.fit(X_train, y_train, 
					epochs=100, 
					batch_size = 1000, 
					validation_split = 0.2, 
					verbose = 0)

개발 기록 / Development log

0.0.1 - 22.03.26

• rosnet.neural_network
  • rosnet.neural_network.Sequential add
  • rosnet.neural_network.layers add
  • rosnet.neural_network.optimizers add